Dose distribution prediction for optimal treamtment of modern external beam radiation therapy for nasopharyngeal carcinoma

Bilel Daoud, Ken’ichi Morooka, Shoko Miyauchi, Ryo Kurazume, Wafa Mnejja, Leila Farhat, Jamel Daoud

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In Intensity-modulated radiation therapy, the planning of the optimal dose distribution for a patient is a complex and time-consuming process. This paper proposes a new automatic method for predicting of dose distribution of Nasopharyngeal carcinoma (NPC) from contoured computer tomography (CT) images. The proposed method consists of two phases: (1) predicting the 2D optimal dose images of each beam from contoured CT images of a patient by convolutional deep neural network model, called OTNet, and (2) integrating the optimal dose images of all the beams to predict the dose distribution for the patient. From the experiments using CT images of 80 NPC patients, our proposed method achieves a good performance for predicting dose distribution compared with conventional predicted dose distribution methods.

Original languageEnglish
Title of host publicationArtificial Intelligence in Radiation Therapy - 1st International Workshop, AIRT 2019, Held in Conjunction with MICCAI 2019, Proceedings
EditorsDan Nguyen, Steve Jiang, Lei Xing
PublisherSpringer
Pages128-136
Number of pages9
ISBN (Print)9783030324858
DOIs
Publication statusPublished - Jan 1 2019
Event1st International Workshop on Connectomics in Artificial Intelligence in Radiation Therapy, AIRT 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019 - Shenzhen, China
Duration: Oct 17 2019Oct 17 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11850 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference1st International Workshop on Connectomics in Artificial Intelligence in Radiation Therapy, AIRT 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019
CountryChina
CityShenzhen
Period10/17/1910/17/19

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Daoud, B., Morooka, K., Miyauchi, S., Kurazume, R., Mnejja, W., Farhat, L., & Daoud, J. (2019). Dose distribution prediction for optimal treamtment of modern external beam radiation therapy for nasopharyngeal carcinoma. In D. Nguyen, S. Jiang, & L. Xing (Eds.), Artificial Intelligence in Radiation Therapy - 1st International Workshop, AIRT 2019, Held in Conjunction with MICCAI 2019, Proceedings (pp. 128-136). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11850 LNCS). Springer. https://doi.org/10.1007/978-3-030-32486-5_16